Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes models trained to estimate UHI in Los Angeles and Seattle, along with open-source software and additional training data for the 50 most populous cities in the contiguous United States. The study demonstrates the application of these methods in evaluating climate change impacts and heat mitigation strategies within high-resolution urban microclimate modeling. The dataset aims to provide a computationally efficient and adaptable solution for urban planners to address various heat planning questions and prioritize heat mitigation strategies. The open-source models, software, and data will contribute to the development of more heat-resilient and sustainable urban environments in the face of climate change.

This data is preliminary and is available to support peer review of an associated manuscript. The data will be finalized upon completion of the peer review.

The Sup3rUHI GitHub repository is under development and will be linked as a resource when complete.